This is AldrinEC’s first assignment for Geog458. Our course page can be accessed here.
This is my favorite video game right now.
The formula to calculate the area of a circle is: \(\pi r^2\)
| Course Name | Quarter Completed |
|---|---|
| CSE 142 | Wi16 |
| MATH 124 | Sp16 |
| GEOG 360 | Au18 |
| GEOG 315 | Au18 |
Source: Aldrin Carbonell
100/10+2
## [1] 12
100/(10+2)
## [1] 8.333333
80/(15+5)*2-30/6
## [1] 3
45+5*2-10
## [1] 45
200-50*2+(5*3)-20
## [1] 95
x=8*3
x+10
## [1] 34
y=6
z=11
x*y
## [1] 144
(z+10)/3
## [1] 7
(y+z)*38
## [1] 646
nums = seq(1, 30)
strings = c("astronaut","ballerina","camera")
length(nums)
## [1] 30
length(strings)
## [1] 3
sum(nums)
## [1] 465
num2 = seq(1, 5)
num3 = seq(6, 10)
num2 + num3
## [1] 7 9 11 13 15
prod = num2 * num3
twoseqs = c(num2, num3)
rows = rbind(num2, num3, prod)
rows
## [,1] [,2] [,3] [,4] [,5]
## num2 1 2 3 4 5
## num3 6 7 8 9 10
## prod 6 14 24 36 50
numsdata = as.data.frame(rows)
numsdata
## V1 V2 V3 V4 V5
## num2 1 2 3 4 5
## num3 6 7 8 9 10
## prod 6 14 24 36 50
this is how to load data into R and how to convert it.
library(tidyverse)
## -- Attaching packages ---------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --
## v ggplot2 3.1.0 v purrr 0.2.5
## v tibble 1.4.2 v dplyr 0.7.8
## v tidyr 0.8.2 v stringr 1.3.1
## v readr 1.3.1 v forcats 0.3.0
## -- Conflicts ------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
object1=read.csv("China_EO_49to17.csv")
chinadata=as_tibble(object1)
rearrange = arrange(chinadata, desc(Year))
rearrange
## # A tibble: 69 x 63
## Year Beijing_Enterpr~ Beijing_Output Tianjin_Enterpr~ Tianjin_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2017 3231 NA 4286 NA
## 2 2016 3340 18087. 5203 27402.
## 3 2015 3548 17450. 5525 28017.
## 4 2014 3686 18453. 5501 28079.
## 5 2013 3641 17371. 5511 26400.
## 6 2012 3692 15596. 5342 23428.
## 7 2011 3746 14514. 5013 20863.
## 8 2010 6884 13700. 7947 16752.
## 9 2009 6890 11039. 8326 13084.
## 10 2008 7205 10413. 7950 12503.
## # ... with 59 more rows, and 58 more variables: Hebei_Enterprise <int>,
## # Hebei_Output <dbl>, Shanxi_Enterprise <int>, Shanxi_Output <dbl>,
## # InnerMongolia_Enterprise <int>, InnerMongolia_Output <dbl>,
## # Liaoning_Enterprise <int>, Liaoning_Output <dbl>,
## # Jilin_Enterprise <int>, Jilin_Output <dbl>,
## # Heilongjiang_Enterprise <int>, Heilongjiang_Output <dbl>,
## # Shanghai_Enterprise <int>, Shanghai_Output <dbl>,
## # Jiangsu_Enterprise <int>, Jiangsu_Output <dbl>,
## # Zhejiang_Enterprise <int>, Zhejiang_Output <dbl>,
## # Anhui_Enterprise <int>, Anhui_Output <dbl>, Fujian_Enterprise <int>,
## # Fujian_Output <dbl>, Jiangxi_Enterprise <int>, Jiangxi_Output <dbl>,
## # Shandong_Enterprise <int>, Shandong_Output <dbl>,
## # Henan_Enterprise <int>, Henan_Output <dbl>, Hubei_Enterprise <int>,
## # Hubei_Output <dbl>, Hunan_Enterprises <int>, Hunan_Output <dbl>,
## # Guangdong_Enterprise <int>, Guangdong_Output <dbl>,
## # Guangxi_Enterprise <int>, Guangxi_Output <dbl>,
## # Hainan_Enterprise <int>, Hainan_Output <dbl>,
## # Chongqing_Enterprise <int>, Chongqing_Output <dbl>,
## # Sichuan_Enterprise <int>, Sichuan_Output <dbl>,
## # Guizhou_Enterprise <int>, Guizhou_Output <dbl>,
## # Yunnan_Enterprise <int>, Yunnan_Output <dbl>, Tibet_Enterprise <int>,
## # Tibet_Output <dbl>, Shaanxi_Enterprise <int>, Shaanxi_Output <dbl>,
## # Gansu_Enterprise <int>, Gansu_Output <dbl>, Qinghai_Enterprise <int>,
## # Qinghai_Output <dbl>, Ningxia_Enterprise <int>, Ningxia_Output <dbl>,
## # Xinjiang_Enterprise <int>, Xinjiang_Output <dbl>
year2k = filter(rearrange, Year >= 2000)
shang.bei = select(year2k, Year, Shanghai_Enterprise, Shanghai_Output, Beijing_Enterprise, Beijing_Output)
shang.bei
## # A tibble: 18 x 5
## Year Shanghai_Enterpri~ Shanghai_Output Beijing_Enterpr~ Beijing_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2017 8122 36094. 3231 NA
## 2 2016 8351 31136. 3340 18087.
## 3 2015 8994 31050. 3548 17450.
## 4 2014 9469 32665. 3686 18453.
## 5 2013 9796 32089. 3641 17371.
## 6 2012 9772 31548. 3692 15596.
## 7 2011 9962 32445. 3746 14514.
## 8 2010 16684 30114. 6884 13700.
## 9 2009 17906 24091. 6890 11039.
## 10 2008 18792 25121. 7205 10413.
## 11 2007 15099 22260. 6397 9648.
## 12 2006 14404 18573. 6400 8210
## 13 2005 14809 15768. 6300 6946.
## 14 2004 15766 12885. 6871 4881.
## 15 2003 11098 10343. 4019 3810.
## 16 2002 10057 7741. 4551 3173.
## 17 2001 9762 7004. 4356 2909.
## 18 2000 8574 6205. 4572 2565.
finaltib = mutate(shang.bei, Output_Ratio = Beijing_Output / Shanghai_Output)
finaltib
## # A tibble: 18 x 6
## Year Shanghai_Enterp~ Shanghai_Output Beijing_Enterpr~ Beijing_Output
## <int> <int> <dbl> <int> <dbl>
## 1 2017 8122 36094. 3231 NA
## 2 2016 8351 31136. 3340 18087.
## 3 2015 8994 31050. 3548 17450.
## 4 2014 9469 32665. 3686 18453.
## 5 2013 9796 32089. 3641 17371.
## 6 2012 9772 31548. 3692 15596.
## 7 2011 9962 32445. 3746 14514.
## 8 2010 16684 30114. 6884 13700.
## 9 2009 17906 24091. 6890 11039.
## 10 2008 18792 25121. 7205 10413.
## 11 2007 15099 22260. 6397 9648.
## 12 2006 14404 18573. 6400 8210
## 13 2005 14809 15768. 6300 6946.
## 14 2004 15766 12885. 6871 4881.
## 15 2003 11098 10343. 4019 3810.
## 16 2002 10057 7741. 4551 3173.
## 17 2001 9762 7004. 4356 2909.
## 18 2000 8574 6205. 4572 2565.
## # ... with 1 more variable: Output_Ratio <dbl>